The most widely used evaluation, initially proposed by Zhang  [1]}, consists in training linear classifiers on top of frozen features on ImageNet.
While widely adopted, this evaluation has been criticized for being somewhat artificial.
A finer study has proposed by Sariyildiz , probing the performance of models when transferring to more distant concepts in ImageNet-22k [2]}.
Many recent works, following Chen  [3]} demonstrate performance on other image classification datasets such as Oxford Flowers [4]}, Oxford Pets [5]}, MNIST [6]} or CIFAR [7]}.
These benchmarks are saturated with near perfect accuracy, and hence offer limited insight about the quality of a method.
